Read in data from Sky Pond (alpine) and The Loch (subalpine)

Sky littoral sites

SB1 <- read_csv(here("data/sky/littoral/SB1_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB2 <- read_csv(here("data/sky/littoral/SB2_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB3 <- read_csv(here("data/sky/littoral/SB3_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB4 <- read_csv(here("data/sky/littoral/SB4_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
SB5 <- read_csv(here("data/sky/littoral/SB5_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))

Loch littoral sites

LB1 <- read_csv(here("data/loch/littoral/LochInlet_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB3 <- read_csv(here("data/loch/littoral/LB3_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB4 <- read_csv(here("data/loch/littoral/LB4_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB5 <- read_csv(here("data/loch/littoral/LB5_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
LB6 <- read_csv(here("data/loch/littoral/LB6_2016.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))

Master littoral

sky_littoral <- bind_rows(SB1, SB2, SB3, SB4, SB5) %>%
  mutate(habitat = "littoral")
loch_littoral <- bind_rows(LB3, LB4, LB5, LB6, LB1) %>%
  mutate(habitat = "littoral")

littoral_master <- bind_rows(sky_littoral,
                             loch_littoral) %>%
  mutate(dateTime = round_date(dateTime, "hour")) #For making joining to pelagic data easier

Sky buoy temperature 2016

# sky_buoy_long_original <-
#   read.table(here("data/sky/pelagic/sky_2016_tempProfile.txt"),
#              sep = ",",
#              header = TRUE) %>%
#   mutate(
#     dateTime = ymd_hms(dateTime),
#     dateTime = with_tz(dateTime, tz = "America/Denver"),
#     dateTime = force_tz(dateTime, "America/Denver")
#   ) %>%
#   # filter(dateTime >= "2016-06-13" &
#   #          dateTime <= "2016-10-30") %>% #ice off and on dates
#   rename(wtr_6.5 = wtr_7.0) %>%
#   pivot_longer(-dateTime, names_to = "depth") %>%
#   mutate(habitat = "pelagic") %>%
#   separate(col = depth,
#            into = c("parameter", "depth"),
#            sep = "_") %>%
#   mutate(parameter = "temperature",
#          lakeID = "SkyPond")
# 
##  Updated Sky Pond 0.5m and 6.5m miniDot data from 2016 through 2018
# surface <- read_csv(here("data/sky/pelagic/sky_0.5m_temp_DO.csv"))%>%
#   mutate(dateTime= mdy_hm(dateTime)) %>%
#   pivot_longer(temp_0.5:DO_0.5,
#                names_to = c("parameter","depth"),
#                names_sep = "_",
#                values_to = "value")
# hypo <- read_csv(here("data/sky/pelagic/sky_6.5m_temp_DO.csv"))%>%
#   mutate(dateTime= mdy_hm(dateTime)) %>%
#   pivot_longer(temp_6.5:DO_6.5,
#                names_to = c("parameter","depth"),
#                names_sep = "_",
#                values_to = "value")
# new_sky <- bind_rows(surface, hypo) %>%
#   mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
#          dateTime = force_tz(dateTime, "America/Denver"),
#          dateTime=round_date(dateTime, "30 min"))
# 
# sky_buoy_long <- new_sky %>%
#   bind_rows(., sky_buoy_long_original %>%
#               select(-habitat) %>%
#               mutate(parameter=recode(parameter, temperature="temp"),
#                      dateTime=round_date(dateTime, "30 min"))%>%
#               filter(!dateTime %in% new_sky$dateTime))
# min(sky_buoy_long$dateTime)
# max(sky_buoy_long$dateTime)
# write_csv(sky_buoy_long, here("data/sky_buoy_long_temp_DO_2016-07-14_to_2018-08-25.csv"))

Read in Sky Pond buoy data 2016-07-14 20:30:00 UTC through 2018-08-25 09:00:00 UTC I commented out the wrangling I did above to combine a few .csv files

sky_buoy_long<-read_csv(here("data/sky_buoy_long_temp_DO_2016-07-14_to_2018-08-25.csv")) %>%
  mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
         dateTime = force_tz(dateTime, "America/Denver"),
         depth = as.character(as.numeric(depth)))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   lakeID = col_character(),
##   dateTime = col_datetime(format = ""),
##   parameter = col_character(),
##   depth = col_double(),
##   value = col_double()
## )
# loch_buoy_long_original <- read.table(here("data/loch/pelagic/loch_2016_tempProfile.txt"), sep=",", header=TRUE) %>%
#   mutate(dateTime = ymd_hms(as.factor(dateTime)),
#          dateTime = force_tz(dateTime, tz="America/Denver"),
#          dateTime = with_tz(dateTime, "America/Denver")) %>%
#   filter(dateTime > "2016-05-31" & dateTime <= "2016-10-30") %>% #ice off and on dates
#   pivot_longer(-dateTime, names_to="depth") %>%
#   mutate(habitat="pelagic") %>%
#   separate(col = depth, into = c("parameter", "depth"), sep = "_") %>%
#   mutate(parameter="temperature",
#          lakeID="TheLoch")
# 
## Updated The Loch 0.5m and 4.5m miniDot data from 2016 through 2018
# surface <- read_csv(here("data/loch/pelagic/loch_0.5m_temp_DO.csv"))%>%
#   mutate(dateTime= mdy_hm(dateTime)) %>%
#   pivot_longer(temp_0.5:DO_0.5,
#                names_to = c("parameter","depth"),
#                names_sep = "_",
#                values_to = "value")
# hypo <- read_csv(here("data/loch/pelagic/loch_4.5m_temp_DO.csv"))%>%
#   mutate(dateTime= mdy_hm(dateTime)) %>%
#   pivot_longer(temp_4.5:DO_4.5,
#                names_to = c("parameter","depth"),
#                names_sep = "_",
#                values_to = "value")
# new_loch <- bind_rows(surface, hypo) %>%
#   mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
#          dateTime = force_tz(dateTime, "America/Denver"),
#          dateTime=round_date(dateTime, "30 min"))
#  
# loch_buoy_long <- new_loch %>%
#   bind_rows(., loch_buoy_long_original %>%
#               select(-habitat) %>%
#               mutate(parameter=recode(parameter, temperature="temp"),
#                      dateTime=round_date(dateTime, "30 min"))%>%
#               filter(!dateTime %in% new_loch$dateTime))
# min(loch_buoy_long$dateTime)
# max(loch_buoy_long$dateTime)
# write_csv(loch_buoy_long, here("data/loch_buoy_long_temp_DO_2016-07-19_to_2018-09-11.csv"))

Read in Sky Pond buoy data 2016-07-14 20:30:00 UTC through 2018-08-25 09:00:00 UTC I commented out the wrangling I did above to combine a few .csv files

loch_buoy_long<-read_csv(here("data/loch_buoy_long_temp_DO_2016-07-19_to_2018-09-11.csv")) %>%
  mutate(dateTime = with_tz(dateTime, tz = "America/Denver"),
         dateTime = force_tz(dateTime, "America/Denver"),
         depth = as.character(as.numeric(depth)))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   lakeID = col_character(),
##   dateTime = col_datetime(format = ""),
##   parameter = col_character(),
##   depth = col_double(),
##   value = col_double()
## )

Master buoy df

buoy_master <-
  bind_rows(loch_buoy_long, sky_buoy_long) %>%
  mutate(
    depth_category = case_when(depth == 0.5 ~ "surface",
                               depth == 4.5 | depth == 6.5 ~ "bottom",
                               depth %in% c("2","4","4") ~ depth,
                               TRUE ~ "other")
    # depth_category = factor(depth_category,
    #                         levels = c("surface", "bottom"))
  ) 

Meterological data

wx <- read_csv(here("data/WY2016to2022_LochVale_WX.csv")) %>%
  mutate(dateTime = mdy_hm(dateTime))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   dateTime = col_character(),
##   T_air = col_double(),
##   RH = col_double(),
##   WDir = col_double(),
##   WSpd = col_double(),
##   WGust = col_double(),
##   SWin = col_logical(),
##   SWout = col_logical(),
##   LWin = col_logical(),
##   LWout = col_logical(),
##   measurement_height = col_double(),
##   station_name = col_character()
## )
## Warning: 56262 parsing failures.
##   row   col           expected actual                                                                                               file
## 34921 SWin  1/0/T/F/TRUE/FALSE  -1    '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34921 SWout 1/0/T/F/TRUE/FALSE  0.036 '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34921 LWin  1/0/T/F/TRUE/FALSE  210   '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34921 LWout 1/0/T/F/TRUE/FALSE  226   '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## 34922 SWin  1/0/T/F/TRUE/FALSE  -1    '/Users/isabellaoleksy/Dropbox/dropbox Research/FABs-DO-concept/data/WY2016to2022_LochVale_WX.csv'
## ..... ..... .................. ...... ..................................................................................................
## See problems(...) for more details.

Chlorophyll data

chl <- read_csv(here("data/AllPelagicCHLA_2016-2021.csv")) %>%
  mutate(date= mdy(date))
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   siteID = col_character(),
##   sampleType = col_character(),
##   date = col_character(),
##   year = col_double(),
##   lakeID = col_character(),
##   easting = col_double(),
##   northing = col_double(),
##   chla_ugL = col_double()
## )

#Sky time series 2016-2018 including under-ice data

Some kind of crazy dynamics going on around ice-off. Interesting that winter 2016-2017 the lake pretty distinctly reverse stratifes, but not in 2017-2018 in 2017, The Loch (subalpine lakes ~200m lower in elevation) had ice-off by 5/16. Usually Sky Pond is ~2 weeks or a little more behind.

In 2018, ice-off occured in The Loch on 5/15, similar to the previous year. Funky DO dynamics in Sky Pond likely shortly after ice off. Lake appears to be well-mixed though.

#Loch time series 2016-2018 including under-ice data

Also some kind of crazy dynamics going on around ice-off. in 2017, The Loch had ice-off by 5/16.

Some wild DO dynamics in late april early may ahead of ice-out.

in 2018 even after ice-off we have periods of anoxia.

Data vis: temperature from littoral zone —————————————————————-

How do the littoral zone temperatures compared to 0.5m temperatures? Include 0.5m depth in the background of each panel

How do the littoral zone diel fluctuations compare to 0.5m temperatures? Include 0.5m depth in the background of each panel

Chlorophyll data

Not entirely sure why there is a gap in 2017 since I know I was collecting data during this time (IAO)

Met data

How do the various meterological variables vary by year? ## Year-round ata

Generally open-water season (June-Aug inclusive)

Not related to the project but interesting just how much of an outlier fall 2020 is (this is when the Cameron Peak and East Troublesome fires were raging)

ggsave("figures/LochVale_wx_seasonal_2016-2021.png", width=15, height=9,units="in", dpi=300)
## Warning: Removed 4970 rows containing non-finite values (stat_ydensity).
## Warning: Removed 4970 rows containing non-finite values (stat_boxplot).